Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques

Qiang Wang, Shuyu Li and Rongrong Li

Energy, 2018, vol. 161, issue C, 821-831

Abstract: Better forecasting energy demand in China and India can help those countries meet future challenges caused by the changes in that demand, as well as inform future global energy needs. In this study, the single-linear, hybrid-linear, and non-linear forecasting techniques based on grey theory are developed to more accurately forecasting energy demand in China and India. These prosed techniques were applied to simulate China’s and India’s energy consumption of China and India between 1990 and 2016. Three standards (trend map, error measure, and fit method) of analyzing quality of forecast technique are used to quantify the quality of these proposed technique. The results show these proposed techniques have a very high degree of fit, a low error rate, and high fitting precision. For example, the mean absolute percent error of single-linear, hybrid-linear, and non-linear techniques are 1.30–3.08%, 0.80–2.57%, and 2.06–2.19%, respectively. The results of optimality analysis show these proposed models can produce reliable forecasting results in China and India, which might be used to forecasting energy demand in other countries/regions. Our forecasting results show the annual growth rate of India’s energy demand from 2017 to 2026 will be 4.49%–5.21% (single-linear), 2.42%–7.04% (hybrid-linear), 0.58%–4.02% (non-linear), respectively. The annual growth rate of China’s energy demand from 2017 to 2026 will be 1.36%–1.70% (single-linear), 1.04%–1.49% (hybrid-linear), 1.80%–2.34% (non-linear), respectively. The growth rate of India’s energy consumption is expected to be 2–4 times that of China from 2017 to 2026, indicating India will become even more important in the global energy market.

Keywords: China and India; Hybrid linear and nonlinear model; Forecasting; Energy security (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (46)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218314658
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:161:y:2018:i:c:p:821-831

DOI: 10.1016/j.energy.2018.07.168

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().